According to the needs of National 863 Project “Underwater Glider Measurement System (2006AA092157)” , National 863 Project “Autonomous observation technology of ocean environment with underwater vehicles(61233013)”,and Open Foundation of State Key Laboratory of Robotics “research of underwater glider adaptive sampling strategy and control technology”, this dissertation conducts a deep research on control strategy of the underwater gliders for ocean observation. The main contents of this dissertation are as follows: 1. Observational architecture analysis of ocean observation system. The architecture of the observation system is based on the function, so the scope and function of each subsystem is defined. Due to the mobile platform, the control and decision making and path planning, and the lack discussion of the marine characteristics, we give discussion about the autonomous platform selection and observation precision requirements with respect to different observation mission. We give discussion about the ocean model, data assimilation, and the sampling data estimation. We expound the relationship between the data estimation results and the tracking process decision. Lastly, we take the near real time tracking of contour line of the temperature field and upwell observation，as an example to design the observation system and tracking process. 2. Analysis of tracking strategy of typical ocean phenomenon. According to micro-scale and meso-scale ocean phenomenon, such as upwelling, thermocline, internal waves, frontal and eddy, the traditional observation method has greatly lack in precision and autonomy. Therefore we analyze these phenomenon and characteristics, and give the tracking and decision strategy of these phenomena. Especially, for China offshore area, we give discussion about the location, scale, and observational threshold for some ocean phenomenon. 3. Research on the underwater glider dynamics model. The difference of the glider is mainly the steering mechanism and the hybrid mode. For the glider which is steered by the internal mass, we establish the glider dynamic model with the Lagrange equation. We compare the simulation result with the experiment data, and give a thorough discussion about the couple of glider hydrodynamic system. Also we consider the ocean current influence, and design a compensation for the net buoyancy influced by the variation of temperature, depth, salty during the glider deeply diving process. 4. Analysis of underwater glider steady gliding state characteristic. Considering that the underwater glider steady state gliding time accounted for a large proportion of the total work time, we have to analyze the relationships between the control inputs, the glider steady gliding state, and its influencing factors. During the observation process, we need to solve the control input with the specific velocity, and angular velocity that got from the path plan system, so we design the iterative algorithm to solve it. 5. Switching control research of underwater glider vertical sawtooth motion. LQR theory is adopted, so as to adjust the sample spatial density during the vertical profile observation. We give the corresponding control strategy based on the two-point boundary problem theory, so as to enhance the position control precision, and resist the ocean current effect. 6. Isoline tracking simulation and analysis of ocean temperature field. Gradient is one of the important characteristic that reflects the property of the phenomena. The tracking process includes the ocean characteristic value estimation, gradient estimation and glider formation control law design. For the former, we utilize multiple underwater glider observation data to estimate the gradient and the scalar value, then we connect it with the tracking target to design the gliders swarm movement direction and speed control law. For the latter, many gliders keep certain formation and rotation mode for tracking. Therefore we design the control law to drive the glider to move along the contour tangent direction, so as to get the biggest observation difference to improve the estimation effect. Finally, we give the simulation with the actual temperature field to verify the validation of the method.